Do continuous word embeddings encode any useful information for constituency parsing? We isolate three ways in which word embeddings might augment a state- of-the-art statistical parser: by connecting out-of-vocabulary words to known ones, by encouraging common behavior among related in-vocabulary words, and by di- rectly providing features for the lexicon. We test each of these hypotheses with a targeted change to a state-of-the-art base- line. Despite small gains on extremely small supervised training sets, we find that extra information from embeddings appears to make little or no difference to a parser with adequate training data. Our results support an overall hypothe- sis that word embeddings import syntac- tic information that is ultimately redun- dant with distinctions learned from...